{"title":"根据卫星数据建立农村社区家庭财富可解释模型的框架","authors":"Emily J. Zuetell;Paulina Jaramillo","doi":"10.1109/TTS.2024.3377541","DOIUrl":null,"url":null,"abstract":"Data-driven policy development and investment are necessary for aligning policies across administrative levels, targeting interventions, and meeting the 2030 Sustainable Development Goals. However, local-level economic well-being data at timely intervals, critical to informing policy development and ensuring equity of outcomes, are unavailable in many parts of the world. Yet, filling these data gaps with black-box predictive models like neural networks introduces risk and inequity to the decision- making process. In this work, we construct an alternative interpretable model to these black-box models to predict household wealth, a key socioeconomic well-being indicator, at 5-km scale from widely available satellite data. Our interpretable model promotes transparency, the identification of potential drivers of bias and harmful outcomes, and inclusive design for human-ML decision-making. We model household wealth as a low- order function of productive land use that can be interpreted and integrated with domain knowledge by decision-makers. We aggregate remotely sensed land cover change data from 2006–2019 to construct an interpretable linear regression model for household wealth and wealth change in Uganda at a 5-km scale with \n<inline-formula> <tex-math>$r^{2}\\,\\,{=}$ </tex-math></inline-formula>\n 72%. Our results demonstrate that there is not a clear performance-interpretability tradeoff in modeling household wealth from satellite imagery at high spatial and temporal resolution. Finally, we recommend a tiered framework to model socioeconomic outcomes from remote sensing data.","PeriodicalId":73324,"journal":{"name":"IEEE transactions on technology and society","volume":"5 1","pages":"36-44"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Framework for the Interpretable Modeling of Household Wealth in Rural Communities From Satellite Data\",\"authors\":\"Emily J. Zuetell;Paulina Jaramillo\",\"doi\":\"10.1109/TTS.2024.3377541\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data-driven policy development and investment are necessary for aligning policies across administrative levels, targeting interventions, and meeting the 2030 Sustainable Development Goals. However, local-level economic well-being data at timely intervals, critical to informing policy development and ensuring equity of outcomes, are unavailable in many parts of the world. Yet, filling these data gaps with black-box predictive models like neural networks introduces risk and inequity to the decision- making process. In this work, we construct an alternative interpretable model to these black-box models to predict household wealth, a key socioeconomic well-being indicator, at 5-km scale from widely available satellite data. Our interpretable model promotes transparency, the identification of potential drivers of bias and harmful outcomes, and inclusive design for human-ML decision-making. We model household wealth as a low- order function of productive land use that can be interpreted and integrated with domain knowledge by decision-makers. We aggregate remotely sensed land cover change data from 2006–2019 to construct an interpretable linear regression model for household wealth and wealth change in Uganda at a 5-km scale with \\n<inline-formula> <tex-math>$r^{2}\\\\,\\\\,{=}$ </tex-math></inline-formula>\\n 72%. Our results demonstrate that there is not a clear performance-interpretability tradeoff in modeling household wealth from satellite imagery at high spatial and temporal resolution. Finally, we recommend a tiered framework to model socioeconomic outcomes from remote sensing data.\",\"PeriodicalId\":73324,\"journal\":{\"name\":\"IEEE transactions on technology and society\",\"volume\":\"5 1\",\"pages\":\"36-44\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on technology and society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10472658/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on technology and society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10472658/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Framework for the Interpretable Modeling of Household Wealth in Rural Communities From Satellite Data
Data-driven policy development and investment are necessary for aligning policies across administrative levels, targeting interventions, and meeting the 2030 Sustainable Development Goals. However, local-level economic well-being data at timely intervals, critical to informing policy development and ensuring equity of outcomes, are unavailable in many parts of the world. Yet, filling these data gaps with black-box predictive models like neural networks introduces risk and inequity to the decision- making process. In this work, we construct an alternative interpretable model to these black-box models to predict household wealth, a key socioeconomic well-being indicator, at 5-km scale from widely available satellite data. Our interpretable model promotes transparency, the identification of potential drivers of bias and harmful outcomes, and inclusive design for human-ML decision-making. We model household wealth as a low- order function of productive land use that can be interpreted and integrated with domain knowledge by decision-makers. We aggregate remotely sensed land cover change data from 2006–2019 to construct an interpretable linear regression model for household wealth and wealth change in Uganda at a 5-km scale with
$r^{2}\,\,{=}$
72%. Our results demonstrate that there is not a clear performance-interpretability tradeoff in modeling household wealth from satellite imagery at high spatial and temporal resolution. Finally, we recommend a tiered framework to model socioeconomic outcomes from remote sensing data.